Differential subcellular localization of phosphorylated neurofilament and tau proteins in degenerating neurons of the human entorhinal cortex.

A panel of novel monoclonal antibodies was tested on the human entorhinal cortex for the recognition of age- and disease-related changes of neurofilament proteins (NF). Several antibodies identified phosphorylated NF-H subunit, which occurred preferentially in those aged between 60 and 80 years and were localized in degenerating neurons. Such neurons also contained neurofibrillary tangles, but neurofilament aggregates did not co-localize with tangles, nor did the quantity nor the number of NF-positive neurons correlate with the severity of Alzheimer's disease. This points to a susceptibility of NF in a subset of neurons for phosphorylation- and metabolically related morphological changes during neurodegeneration.

Lentiviral Vector Tropism In Degenerating Retinas

Introduction: In normal mice, lentiviral vector (LV) shows a great efficiency to infect the RPE cells, but transduces retinal neurons more efficiently during development. Here, we investigated the tropism of LV in the degenerating retina of mice, knowing that the retina structure changes during degeneration. We postulated that the viral transduction would be increased by the alteration of the interphotoreceptor matrix (IPM). We tested two different LV-pseudotypes using the VSVG and the Mokola envelopes. Methods: Subretinal injections were performed in wild-type (C57/Bl6) and rhodopsin knockout (Rho-/-) mice. We injected LV-VSVG-EFS-GFPII into 3.3-4.9 month old mice and LV-VSVG-Rho-GFP into 1-1.4 month old mice to target the photoreceptors (PR). LV-MOK-CMV-GFP was injected into 2.4-3.3 months old mice. We sacrificed the animals one week post injection, used immunohistochemistry to identify the transduced cells, and investigated the OLM integrity. Results: Using LV-VSVG-EFS-GFPII into 3.3-4.9 months mice, we observed significant retinal and RPE transduction in Rho-/- mice. However, the retinas showed transduction mainly at the injection's site. We mostly observed GFP+ cells having a Müller cell morphology. Using LV-MOK-CMV-GFP into 2.4-3.3 months mice, we evidenced the same pattern of viral infection, but with more Müller cells targeted by the virus. Using LV-VSVG-Rho-GFP into 1-1.4 month old mice, we don't note any difference between Rho-/- and wild-type mice for transduced cells. The IPM stained with ZO1 appears irregular into the 4.9 months old Rho-/- mice; for the youngest mice (Rho-/- and C57/Bl6), there is no modification of the IPM. Conclusion: The degeneration improves retinal cells transduction due to the alteration of the IPM in old Rho-/- mice. Müller cells seem (by morphological evidences) to be the principal cells expressing the transgene. The LV with Mokola envelope can transduce Müller cells in a degenerating retina with an intact IPM. In 1 month old mice, the degeneration doesn't enhance the transduction in rod PR probably because the IPM is not yet altered. The possibility to target photoreceptors at a later stage of the degeneration is under investigation.

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

In the name of democracy: Disobedience and value-oriented citizenship

Recently, some scholars have highlighted a paradoxical phenomenon existing in democratic systems:Those people who show the greatest support for democracy are also those most willing to protestagainst the authority and to question it. However, if we consider the tasks of contemporary democraticcitizenship in a social-psychological perspective, this apparent paradox becomes understandable.Obedience to authority may ensure the continuity of social and group life, but disobedience may becrucial in stopping the authority relationship from degenerating into an authoritarian one. FollowingKelman and Hamilton's analysis of legitimacy dynamics, we consider how actions of disobediencemay serve the defence of democracy. In particular, by considering the different ways in which peoplerelate to the political system, the relevance of so-called value-oriented citizens in supportingdemocracy will be considered.

Catabolic ornithine carbamoyltransferase of Pseudomonas aeruginosa. Importance of the N-terminal region for dodecameric structure and homotropic carbamoylphosphate cooperativity.

Pseudomonas aeruginosa has an anabolic (ArgF) and a catabolic (ArcB) ornithine carbamoyltransferase (OTCase). Despite extensive sequence similarities, these enzymes function unidirectionally in vivo. In the dodecameric catabolic OTCase, homotropic cooperativity for carbamoylphosphate strongly depresses the anabolic reaction; the residue Glu1O5 and the C-terminus are known to be essential for this cooperativity. When Glu1O5 and nine C-terminal amino acids of the catabolic OTCase were introduced, by in vitro genetic manipulation, into the closely related, trimeric, anabolic (ArgF) OTCase of Escherichia coli, the enzyme displayed Michaelis-Menten kinetics and no cooperativity was observed. This indicates that additional amino acid residues are required to produce homotropic cooperativity and a dodecameric assembly. To localize these residues, we constructed several hybrid enzymes by fusing, in vivo or in vitro, the E. coli argF gene to the P. aeruginosa arcB gene. A hybrid enzyme consisting of 101 N-terminal ArgF amino acids fused to 233 C-terminal ArcB residues and the reciprocal ArcB-ArgF hybrid were both trimers with little or no cooperativity. Replacing the seven N-terminal residues of the ArcB enzyme by the corresponding six residues of E. coli ArgF enzyme produced a dodecameric enzyme which showed a reduced affinity for carbamoylphosphate and an increase in homotropic cooperativity. Thus, the N-terminal amino acids of catabolic OTCase are important for interaction with carbamoylphosphate, but do not alone determine dodecameric assembly. Hybrid enzymes consisting of either 26 or 42 N-terminal ArgF amino acids and the corresponding C-terminal ArcB residues were both trimeric, yet they retained some homotropic cooperativity. Within the N-terminal ArcB region, a replacement of motif 28-33 by the corresponding ArgF segment destabilized the dodecameric structure and the enzyme existed in trimeric and dodecameric states, indicating that this region is important for dodecameric assembly. These findings were interpreted in the light of the three-dimensional structure of catabolic OTCase, which allows predictions about trimer-trimer interactions. Dodecameric assembly appears to require at least three regions: the N- and C-termini (which are close to each other in a monomer), residues 28-33 and residues 147-154. Dodecameric structure correlates with high carbamoylphosphate cooperativity and thermal stability, but some trimeric hybrid enzymes retain cooperativity, and the dodecameric Glu1O5-->Ala mutant gives hyperbolic carbamoylphosphate saturation, indicating that dodecameric structure is neither necessary nor sufficient to ensure cooperativity.

Obedience-disobedience dynamic and the role of responsibility

Obedience has been thoroughly studied in social psychology, both in its positive and negative aspects. Nevertheless, in these empirical studies disobedience has been considered to be the opposite of obedience and indeed its negation. Instead, some recent studies suggest that if obedience to authority is important in ensuring the continuity of social and group life, disobedience is crucial, under some circumstances, in stopping the authority relationship from degenerating into an authoritarian relationship. In this perspective, disobedience may be conceived of as a protest undermining the legitimacy of authority, or else it can represent an instrument of the community for controlling the legitimacy of the authority's demands, becoming a factor safeguarding against authoritarianism. The aim of the present study was to empirically verify the dynamics existing between disobedience and obedience. The results show that people who attach importance to both obedience and disobedience in the relationship between the individual and society recognize the importance of democratic values and consider themselves responsible for the defence of human rights. Instead, people who only recognize the value of obedience and consider disobedience as a threat to the status quo are more authoritarian, individualistic people.

The expression of nuclear 3,5,3' triiodothyronine receptors is induced in Schwann cells by nerve transection.

The effects of thyroid hormones on the nervous system are mediated by the presence of nuclear T3 receptors (NT3R). In this study, the expression of NT3R was investigated in spinal cord, dorsal root ganglia (DRG), or sciatic nerve of adult rats after immunostaining with a 2B3-NT3R monoclonal antibody which recognizes both alpha and beta types of NT3R. The specificity of this monoclonal antibody was confirmed by Western blots. The 2B3-NT3R monoclonal antibody recognized one band corresponding to a molecular weight of 57 kDa in extract of spinal cord or DRG. No staining was observed on immunoblot of intact sciatic nerve. In the spinal cord, the nuclei of the neurons and glial cells including both astrocytes and oligodendrocytes exhibited 2B3-NT3R immunoreactivity. While all the nuclei of the DRG sensory neurons expressed the NT3R, all the nuclei of the satellite and Schwann cells were devoid of any immunoreaction. In the sciatic nerve, the nuclei of the Schwann cells also lacked 2B3-NT3R-immunoreactivity. After sciatic nerve transection in vivo, Schwann cell nuclei, which never expressed NT3R in intact nerves of adult rats, displayed a clear 2B3-NT3R immunoreaction in proximal and distal stumps adjacent to the section. Double immunostaining with antibodies raised to 3-sulfogalactosylceramide or S100 confirmed that most of the NT3R containing nuclei belong to Schwann cells. In dissociated cell cultures grown in vitro from sciatic nerves, Schwann cells exhibited 2B3-NT3R immunoreactivity. These data suggest that the inhibition of NT3R expression in Schwann cells ensheathing axons in intact nerve is reversed when the axons are degenerating or lacking.(ABSTRACT TRUNCATED AT 250 WORDS)

Differential distribution of thyroid hormone receptor isoform in rat dorsal root ganglia and sciatic nerve in vivo and in vitro.

Using autoradiographic techniques carried out under precise conditions we previously demonstrated that both sensory neurons and peripheral glial cells in dorsal root ganglia (DRG) or sciatic nerve, possess specific [125I]-labeled T3 binding sites. Thyroid hormone receptors (TR) include several isoforms (TR alpha(1), TR alpha(2), TR beta(1), TR beta(2...)) The present study demonstrates that while sensory neurons and peripheral glial cells both possess functional TR, they express a differential expression of TR isoforms. Using a panel of antisera to specific for the TR alpha-common (alpha(1) and alpha(2)), TR alpha-1 or TR beta-1 isoforms, we detected TRs isoform localization at the cellular level during DRG and sciatic nerve development and regeneration. Immunohistochemical analysis revealed that during embryonic life, sensory neurons express TR alpha-common and TR beta-1 rather than TR alpha-1. The number of TR alpha-common and TR beta-1 positive neurons as well as the intensity of labeling increased during the first two postnatal weeks and remained more or less stable in adult life. TR alpha-1 immunoreactivity, which was undetectable in embryonic sensory neurons, became discreetly visible in neurons after birth. In developing DRG and sciatic nerves, Schwann cells exhibited TR alpha-common and TR alpha-1 rather than TR beta-1 immunolabeling. The appearance of TR alpha-common and alpha-1 isoform immunoreactivity in the sciatic nerve was restricted to a short period ranging from E17 up to two postnatal weeks. By comparing TR alpha-common and TR alpha-1 immunostaining we can deduce that Schwann cells primarily express TR alpha-1. Afterwards, in adult rat sciatic nerve TR alpha isoforms was no more detected. However transection of sciatic nerve caused a reexpression of TR alpha isoforms in degenerating nerve. The prevalence of TR alpha in Schwann cells in vivo was correlated with in vitro results. The differential expression of TR alpha and beta by sensory neurons and Schwann cells indicates that the feedback regulation of circulating thyroid hormone could occur by binding to either the alpha or beta TR isoforms. Moreover, the presence of multiple receptor isoforms in developing sensory neurons suggests that thyroid hormone uses multiple signaling pathways to regulate DRG and sciatic nerve development.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Harmonic active contours.

We propose a segmentation method based on the geometric representation of images as 2-D manifolds embedded in a higher dimensional space. The segmentation is formulated as a minimization problem, where the contours are described by a level set function and the objective functional corresponds to the surface of the image manifold. In this geometric framework, both data-fidelity and regularity terms of the segmentation are represented by a single functional that intrinsically aligns the gradients of the level set function with the gradients of the image and results in a segmentation criterion that exploits the directional information of image gradients to overcome image inhomogeneities and fragmented contours. The proposed formulation combines this robust alignment of gradients with attractive properties of previous methods developed in the same geometric framework: 1) the natural coupling of image channels proposed for anisotropic diffusion and 2) the ability of subjective surfaces to detect weak edges and close fragmented boundaries. The potential of such a geometric approach lies in the general definition of Riemannian manifolds, which naturally generalizes existing segmentation methods (the geodesic active contours, the active contours without edges, and the robust edge integrator) to higher dimensional spaces, non-flat images, and feature spaces. Our experiments show that the proposed technique improves the segmentation of multi-channel images, images subject to inhomogeneities, and images characterized by geometric structures like ridges or valleys.

Relationship between imatinib population pharmacokinetics and alpha-1-acid glycoprotein

Objectives: Considering the large inter-individual differences in the function of the systems involved in imatinib disposition, exposure to this drug can be expected to vary widely among patients. Among those known systems is alpha-1-acid glycoprotein (AGP), a circulating protein that strongly binds imatinib. This observational study aimed to explore the influence of plasma AGP on imatinib pharmacokinetics. Methods: A population pharmacokinetic analysis was performed using NONMEM based on 278 plasma samples from 51 oncologic patients, for whom both total imatinib and AGP plasma concentrations were measured. The influence of this biological covariate on oral clearance and volume of distribution was examined. Results: A one-compartment model with first-order absorption appropriately described the data. A hyperbolic relationship between plasma AGP levels and oral clearance, as well as volume of distribution was observed. A mechanistic approach was built up, postulating that only the unbound imatinib concentration was able to undergo first-order elimination through an unbound clearance process, and integrating the dissociation constant as a parameter in the model. This approach allowed determining an average (± SEM) free clearance of 1310 (± 172) L/h and a volume of distribution of 301 (± 23) L. By comparison, the total clearance previously determined was 14 (± 1) L/h. Free clearance was affected by body weight and pathology diagnosis. Moreover, this model provided consistent estimates of the association constant between imatinib and AGP (5.5?106 L/mol) and of the average in vivo free fraction of imatinib (1.1%). The variability observed (17% for free clearance and 66% for volume of distribution) was less than the one previously reported without considering AGP impact. AGP explained indeed about one half of the variability observed in total imatinib disposition. Conclusion: Such findings clarify in part the in vivo impact of protein binding on imatinib disposition and might raise again the question whether high levels of AGP could represent a resistance factor to imatinib. This remains however questionable, as it is not expected to affect free drug concentrations. On the other hand, would imatinib be demonstrated as a drug requiring therapeutic drug monitoring, either the measurement of free concentration or the correction of the total concentration by the actual AGP plasma levels should be considered for accurate interpretation of the results.

Differential proteoglycan expression in two spinal cord regions after dorsal root injury.

Dorsal root injury leads to reactive gliosis in the spinal cord dorsal root entry zone and dorsal column, two regions that undergo Wallerian degeneration, but have distinct growth-inhibitory properties. This disparity could in part be due to differences in the number of degenerating sensory fibers, differences in glial cell activation, and/or to differential expression of growth-inhibitory molecules such as chondroitin sulfate proteoglycans. Laser capture microdissection of these two spinal cord white matter regions, followed by quantitative analysis of mRNA expression by real-time PCR, revealed that glial marker transcripts were differentially expressed post-injury and that the chondroitin sulfate proteoglycans Brevican and Versican V1 and V2 were preferentially up-regulated in the dorsal root entry zone, but not the dorsal column. These results indicate that reactive gliosis differs between these two regions and that Brevican and Versican are potential key molecules participating in the highly inhibitory properties of the dorsal root entry zone.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.